Upload app.py

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoProcessor, TextIteratorStreamer
+from threading import Thread
+import re
+import time 
+from PIL import Image
+import torch
+import spaces
+
+processor = AutoProcessor.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True)
+model_od = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-Object-Detection-v2", trust_remote_code=True)
+
+model.to("cuda:0")
+model_od.to("cuda:0")
+
+@spaces.GPU
+def bot_streaming(message, history, max_tokens, temperature, top_p, top_k, repetition_penalty):
+ # print(message)
+ if message.files:
+ image = message.files[-1].path
+ else:
+ # if there's no image uploaded for this turn, look for images in the past turns
+ # kept inside tuples, take the last one
+ for hist in history:
+ if type(hist[0])==tuple:
+ image = hist[0][-1].path
+
+ if image is None:
+ gr.Error("Lütfen önce bir resim yükleyin.")
+ 
+ prompt = f"{message.text}"
+ image = Image.open(image).convert("RGB")
+ inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda:0")
+
+ streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
+ generation_kwargs = dict(
+ inputs, streamer=streamer, max_new_tokens=max_tokens, 
+ do_sample=True, temperature=temperature, top_p=top_p, 
+ top_k=top_k, repetition_penalty=repetition_penalty
+ )
+ generated_text = ""
+
+ thread = Thread(target=model.generate, kwargs=generation_kwargs)
+ thread.start()
+
+ text_prompt = f"{message.text}\n"
+
+ buffer = ""
+ for new_text in streamer:
+ buffer += new_text
+ generated_text_without_prompt = buffer[len(text_prompt):]
+ 
+ time.sleep(0.04)
+ yield generated_text_without_prompt
+
+
+gr.set_static_paths(paths=["static/images/"])
+logo_path = "static/images/logo-color-v2.png"
+
+PLACEHOLDER = f"""
+<div style="display: flex; flex-direction: column; align-items: center; text-align: center; margin: 30px">
+ <img src="/file={logo_path}" style="width: 60%; height: auto;">
+ <h3>Resim yükleyin ve bir soru sorun</h3>
+</div>
+"""
+
+# with gr.Blocks() as demo:
+ # with gr.Tab("Open-ended Questions (Soru-cevap)"):
+with gr.Accordion("Generation parameters", open=False) as parameter_accordion:
+ max_tokens_item = gr.Slider(64, 1024, value=512, step=64, label="Max tokens")
+ temperature_item = gr.Slider(0.1, 2, value=0.6, step=0.1, label="Temperature")
+ top_p_item = gr.Slider(0, 1.0, value=0.9, step=0.05, label="Top_p")
+ top_k_item = gr.Slider(0, 100, value=50, label="Top_k")
+ repeat_penalty_item = gr.Slider(0, 2, value=1.2, label="Repeat penalty")
+
+demo = gr.ChatInterface(
+ title="TraVisionLM - Turkish Visual Language Model",
+ description="",
+ fn=bot_streaming,
+ chatbot=gr.Chatbot(placeholder=PLACEHOLDER, scale=1), 
+ # examples=[{"text": "", "files":[""]},{"text": "", "files":[""]}], 
+ additional_inputs=[max_tokens_item, temperature_item, top_p_item, top_k_item, repeat_penalty_item],
+ additional_inputs_accordion=parameter_accordion, 
+ stop_btn="Stop Generation", 
+ multimodal=True
+)
+
+ # with gr.Tab("Object Detection (Obje Tespiti)"):
+ # gr.Image("tiger.jpg")
+ # gr.Button("New Tiger")
+
+# demo = gr.ChatInterface(fn=bot_streaming, title="TraVisionLM - Turkish Visual Language Model", 
+# # examples=[{"text": "", "files":[""]},{"text": "", "files":[""]}], 
+# description="",
+# additional_inputs=[
+# gr.Slider(64, 1024, value=512, step=64, label="Max tokens"),
+# gr.Slider(0.1, 2, value=0.6, step=0.1, label="Temperature"),
+# gr.Slider(0, 1.0, value=0.9, step=0.05, label="Top_p"),
+# gr.Slider(0, 100, value=50, label="Top_k"),
+# gr.Slider(0, 2, value=1.2, label="Repeat penalty"),
+# ],
+# additional_inputs_accordion_name="Text generation parameters",
+# # additional_inputs_accordion=
+# stop_btn="Stop Generation", multimodal=True)
+demo.launch(allowed_paths="")